A partially observable Markov decision process (POMDP) is a powerful framework for modelling sequential decision making processes under uncertainty with the goal of maximizing total reward. POMDPs have been an active research area and considerable progress has been made on solving POMDPs [1]}, [2]}, [3]}.
While our problem setting can be considered as a POMDP with continuous state, observation and action spaces, our problem also includes a large number of users. Since the number of possible states in this POMDP increases exponentially with the number of users, standard POMDP algorithms are computationally infeasible for our problem.
